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Airborne point cloud roof plane segmentation method based on octree and boundary optimization

A plane segmentation and boundary optimization technology, applied in image analysis, computer parts, character and pattern recognition, etc., can solve problems such as inaccurate roof plane extraction, achieve important market value, solve poor robustness, and solve boundary inaccuracy Effect

Active Publication Date: 2020-06-12
WUHAN UNIV
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Problems solved by technology

[0007] In order to solve the problem of inaccurate roof plane extraction, the present invention provides an airborne point cloud roof plane segmentation method based on octree and boundary optimization

Method used

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  • Airborne point cloud roof plane segmentation method based on octree and boundary optimization
  • Airborne point cloud roof plane segmentation method based on octree and boundary optimization
  • Airborne point cloud roof plane segmentation method based on octree and boundary optimization

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Embodiment

[0057] The embodiment specifically includes the following sub-steps:

[0058] Step 1.1, read the original file of unsequential point cloud data to be processed, store the spatial coordinate value of each point in turn, and obtain the original point cloud point set.

[0059] Step 1.2: Determine the range of point cloud data coordinate values ​​according to the position of the extreme points of the original point cloud coordinates, and construct the vertex position coordinates and dimensions of the smallest cuboid that can contain all point cloud regions. The coordinates of the cuboid are required to be integers.

[0060] Step 1.3, according to the size of the cuboid, it is cut into several sizes of M×M (M=2 m ) of the small cube, denoted as B i , where m≥1, you can get the cube set B={B i}, build a set P that saves the parameters of the planar patch rough . During specific implementation, those skilled in the art can preset the value of m, and i is the label of the small cu...

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Abstract

The invention provides an airborne point cloud roof plane segmentation method based on an octree and boundary optimization. Multi-scale initial plane patch extraction based on an octree is carried out, and the method comprises the following steps: iteratively dividing the non-sequence point cloud into plane patches with different scales according to a spatial position according to a data structureof the octree, and selecting a plane patch with planarity from the plane patches as an initial plane patch obtained by data preprocessing; aggregating the initial plane pieces according to the adjacent relationship and the parameter similarity through hierarchical clustering to form an initial plane; merging the points which do not belong to the planar sheet into an initial plane through regionalgrowth; and plane boundary point re-classification based on energy optimization comprises the steps of converting a plane boundary point re-classification problem into an energy optimization problemthrough an energy function, optimizing a plane boundary obtained by region growth, and obtaining a final plane segmentation result. According to the method, the problems of poor robustness and inaccurate boundary of seed points growing in a region in point cloud plane segmentation are solved, and an optimal plane segmentation result is obtained.

Description

technical field [0001] The invention belongs to the technical field of airborne LiDAR point cloud data processing, in particular to an airborne point cloud roof plane segmentation method based on octree and boundary optimization. Background technique [0002] Point cloud data has gradually become the main data for building 3D building models because of its direct access to 3D information of buildings and rich details. Roof plane segmentation is one of the key issues for building accurate 3D building models from airborne lidar data. Improve the speed and efficiency of 3D reconstruction. At present, there are three commonly used methods for plane segmentation of 3D point clouds, based on random sampling consensus algorithm (RANdom Sample Consensus, RANSAC), algorithm based on Hough transform, and region growing method. In addition, many improved algorithms have emerged based on these three methods. [0003] Consensus algorithm based on random sampling: It is an iterative meth...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/11G06T17/00G06K9/62
CPCG06T7/11G06T17/005G06T2207/10028G06F18/231G06F18/24147Y02A90/10
Inventor 姚剑薛婧雅李礼蒋佳芹
Owner WUHAN UNIV
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